code: def get_sigmas(self, n, device="cpu"):
      ramp = torch.linspace(0, 1, n, device=device)
      min_inv_rho = self.sigma_min ** (1 / self.rho)
      max_inv_rho = self.sigma_max ** (1 / self.rho)
      sigmas = (
          max_inv_rho + ramp * (min_inv_rho - max_inv_rho)
      ) ** self.rho
      return sigmas
  
file_path: generative-models/sgm/modules/diffusionmodules/discretizer.py
imports: from abc import abstractmethod
  from functools import partial
  import numpy
  import torch
  from ...modules.diffusionmodules.util import make_beta_schedule
  from ...util import append_zero
label_desc: Use image node_id 2 for calling the EDMDiscretization obj's underlying member method code with example usage: obj.get_sigmas(n, device) and returns: sigmas